TY - GEN
T1 - Knowledge Graphs-Based Halal Food Provider Information Search System at Surabaya
AU - Bhamakerti, Ganendra Aby
AU - Rakhmawati, Nur Aini
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In Indonesia, approximately 10% of products possess halal certification. Consequently, there is a need for a solution, specifically a system capable of retrieving information from halal food providers and generating recommendations. We crawl the BPJPH API. Subsequently, the ranking of food providers is determined using the Node Similarity algorithm and graph embedding utilizing the Node2vec and Fast Random Projection algorithms in conjunction with the K-Nearest Neighbors algorithm. A simple search engine application was then developed using StreamLit, an open-source Python-based application framework designed to facilitate the creation of web applications in the fields of machine learning and data science. Application development also incorporates SQLiteStudio for database creation. Following the search engine application design process, an evaluation stage was conducted using two metrics: Mean Reciprocal Rank (MRR), a metric to assess each process that gives potential answers to a query arranged in order of their likelihood of being correct, and Normalized Discounted Cumulative Gain (NDCG), a metric that evaluates performance of a ranking list by penalizing incorrect sequence in the list. Overall, the performances of systems that use Node Similarity and K-Nearest Neighbors with Fast Random Projection algorithms are generally better than the performance of system that uses K-Nearest Neighbors with Node2Vec Algorithms.
AB - In Indonesia, approximately 10% of products possess halal certification. Consequently, there is a need for a solution, specifically a system capable of retrieving information from halal food providers and generating recommendations. We crawl the BPJPH API. Subsequently, the ranking of food providers is determined using the Node Similarity algorithm and graph embedding utilizing the Node2vec and Fast Random Projection algorithms in conjunction with the K-Nearest Neighbors algorithm. A simple search engine application was then developed using StreamLit, an open-source Python-based application framework designed to facilitate the creation of web applications in the fields of machine learning and data science. Application development also incorporates SQLiteStudio for database creation. Following the search engine application design process, an evaluation stage was conducted using two metrics: Mean Reciprocal Rank (MRR), a metric to assess each process that gives potential answers to a query arranged in order of their likelihood of being correct, and Normalized Discounted Cumulative Gain (NDCG), a metric that evaluates performance of a ranking list by penalizing incorrect sequence in the list. Overall, the performances of systems that use Node Similarity and K-Nearest Neighbors with Fast Random Projection algorithms are generally better than the performance of system that uses K-Nearest Neighbors with Node2Vec Algorithms.
KW - Graph Embedding
KW - Halal
KW - Halal Food Providers
KW - Halal Restaurant Certification
KW - Knowledge Graph
UR - http://www.scopus.com/inward/record.url?scp=85216103348&partnerID=8YFLogxK
U2 - 10.1109/ICDABI63787.2024.10800248
DO - 10.1109/ICDABI63787.2024.10800248
M3 - Conference contribution
AN - SCOPUS:85216103348
T3 - 2024 5th International Conference on Data Analytics for Business and Industry, ICDABI 2024
SP - 132
EP - 138
BT - 2024 5th International Conference on Data Analytics for Business and Industry, ICDABI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Data Analytics for Business and Industry, ICDABI 2024
Y2 - 23 October 2024 through 24 October 2024
ER -